Pan Zhenyu, You Haisheng, Bu Qingting, Feng Xiaojie, Zhao Fanfan, Li Yuanjie, Lyu Jun
Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
J Cancer. 2019 Aug 28;10(21):5299-5305. doi: 10.7150/jca.32741. eCollection 2019.
: The objective of this study was to develop and validate a nomogram for predicting the cancer-specific survival (CSS) in patients with Wilms' tumor (WT). : Patients with WT diagnosed between 2002 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were divided randomly into training and validation cohorts in this study. Multivariate Cox regression analysis was used to screen variables. A Cox proportional-hazards regression model and a nomogram were constructed based on variables that significantly affected the CSS in the training cohort. The nomogram for distinguishing and predicting the CSS was evaluated using the concordance index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC), and calibration plots. : In total, 1631 patients from the SEER database were enrolled, with 1141 categorized into the training cohort and 490 into the validation cohort. All significant variables associated with CSS-age, the number of examined lymph nodes, SEER stage, and tumor size-were included in the nomogram. The C-index values of the nomogram in the training and validation cohorts were 0.746 and 0.703, respectively. The 3-, 5-, and 10-year AUCs were 0.755, 0.749, and 0.724, respectively, in the training cohort, and 0.718, 0.707, and 0.718 in the validation cohort. The calibration plots indicated the nomogram could accurately predict the 3-, 5-, and 10-year CSS. : We have developed and validated the first nomogram for predicting the survival of WT patients. The nomogram is a reliable tool for distinguishing and predicting the CSS in patients with WT. Information provided by the nomogram may help to improve the clinical practices related to WT.
本研究的目的是开发并验证一种用于预测肾母细胞瘤（WT）患者癌症特异性生存（CSS）的列线图。在本研究中，将2002年至2015年期间在监测、流行病学和最终结果（SEER）数据库中诊断为WT的患者随机分为训练队列和验证队列。采用多变量Cox回归分析筛选变量。基于在训练队列中显著影响CSS的变量构建Cox比例风险回归模型和列线图。使用一致性指数（C指数）、时间依赖性受试者工作特征曲线下面积（AUC）和校准图对用于区分和预测CSS的列线图进行评估。
总共纳入了SEER数据库中的1631例患者，其中1141例被分类到训练队列，490例被分类到验证队列。与CSS相关的所有显著变量——年龄、检查的淋巴结数量、SEER分期和肿瘤大小——都被纳入了列线图。列线图在训练队列和验证队列中的C指数值分别为0.746和0.703。训练队列中3年、5年和10年的AUC分别为0.755、0.749和0.724，验证队列中分别为0.718、0.707和0.718。校准图表明列线图可以准确预测3年、5年和10年的CSS。
我们已经开发并验证了首个用于预测WT患者生存的列线图。该列线图是区分和预测WT患者CSS的可靠工具。列线图提供的信息可能有助于改善与WT相关的临床实践。
